Mathematics for Machine Learning with Python- CST284 KTU Minor - Dr. Binu V P -9847390760

  Introduction About Me Syllabus Course Outcomes and Model Question Paper Question Paper July 2021 and evaluation scheme Question Paper June 2022 and evaluation scheme Overview of Machine Learning What is Machine Learning (video) Learn the Seven Steps in Machine Learning (video) Linear Algebra in Machine Learning Module I- Linear Algebra 1.Geometry of Linear Equations (video-Gilbert Strang) 2.Elimination with Matrices (video-Gilbert Strang) 3.Solving System of equations using Gauss Elimination Method 4.Row Echelon form and Reduced Row Echelon Form -Python Code 5.Solving system of equations Python code 6. Practice problems Gauss Elimination ( contact) 7.Finding Inverse using Gauss Jordan Elimination  (video) 8.Finding Inverse using Gauss Jordan Elimination-Python code Vectors in Machine Learning- Basics 9.Vector spaces and sub spaces 10.Linear Independence 11.Linear Independence, Basis and Dimension (video) 12.Generating set basis and span 13.Rank of a Matrix 14.Linear Mapping and Matri

 

 

As data scientists we work with data in various formats such as text images and numerical values We often use vectors to represent data in a structured and efficient manner especially in machine learning applications In this blog post we will explore what vectors are in terms of machine learning their significance and how they are used What is a Vector? In mathematics, a vector is a mathematical object that has both magnitude and direction. In machine learning, a vector is a mathematical representation of a set of numerical values. Vectors are usually represented as arrays or lists of numbers, and each number in the list represents a specific feature or attribute of the data. For example, suppose we have a dataset of houses, and we want to predict their prices based on their features such as the number of bedrooms, the size of the house, and the location. We can represent each house as a vector, where each element of the vector represents a specific feature of the house, such as the nu

Gradient Descent Gradient Descent is a generic optimization algorithm capable of finding optimal solutions to a wide range of problems. The general idea is to tweak parameters iteratively in order to minimize the cost function. An important parameter of Gradient Descent (GD) is the size of the steps, determined by the learning rate hyperparameters. If the learning rate is too small, then the algorithm will have to go through many iterations to converge, which will take a long time, and if it is too high we may jump the optimal value. Note: When using Gradient Descent, we should ensure that all features have a similar scale (e.g. using Scikit-Learn’s StandardScaler class), or else it will take much longer to converge. Types of Gradient Descent: Typically, there are three types of Gradient Descent:  Batch Gradient Descent Stochastic Gradient Descent Mini-batch Gradient Descent Stochastic Gradient Descent (SGD): The word ‘stochastic‘ means a system or process linked with a random probabil